US-20260127209-A1 - PROCESS AND SYSTEM FOR SECURELY SEARCHING AND SUMMARIZING DATA FROM SOURCE SYSTEMS

Abstract

A system and process is described for securely searching and summarizing results from organizational source systems. The system and process retrieves live data from multiple disparate systems of record using a retrieval-augmented generation (RAG) approach, AI agents, and an AI Assistant Orchestrator. The AI Assistant Orchestrator is aware of all of capabilities of the AI agents and the systems of record including security, data structures, and communication protocols, and automatically generates requests for the AI agents and queries for the systems of record in their respective correct format and execution order including adaptive calls based on information for preceding request or execution steps. The method also correlates data identities between the systems of record and the AI agents using dynamically generated harmonization steps with generative AI to obtain retrieved data and correlated information; and provides the retrieved data and the correlated information to the operator.

Inventors

• Amitkumar C. Jain
• Binh Vu

Assignees

• CHEVRON U.S.A. INC.

Dates

Publication Date

20260507

Application Date

20251028

Claims (9)

1. 1 . A system for secure data retrieval from multiple disparate systems of record using Artificial Intelligence (AI), the system comprising: one or more physical processors configured by machine-readable instructions to: a. receive input from an operator; b. based on the input from the operator, retrieve live data from the multiple disparate systems of record using a retrieval-augmented generation (RAG) approach, AI agents, and an AI Assistant Orchestrator, wherein the AI Assistant Orchestrator is aware of all of capabilities of the AI agents and the multiple disparate systems of record including security, data structures, and communication protocols, and wherein the AI Assistant Orchestrator automatically generates requests for the AI agents and queries for the multiple disparate systems of record in their respective correct format and execution order including adaptive calls based on information for preceding request or execution steps; c. correlate data identities between the multiple disparate systems of record and the AI agents using dynamically generated harmonization steps with generative AI to obtain retrieved data and correlated information; and d. provide the retrieved data and the correlated information to the operator.
2. 2 . The system of claim 1 , wherein the one or more physical processors are further configured by the machine-readable instructions to dynamically generate harmonization steps using generative AI to ensure data consistency across different systems of records.
3. 3 . The system of claim 1 , wherein the one or more physical processors are further configured by the machine-readable instructions to provide a verification interface for the operator to review steps taken to retrieve and correlate the retrieved data.
4. 4 . The system of claim 1 , wherein the retrieval-augmented generation (RAG) approach includes: a. generating and executing queries to retrieve data from the multiple disparate systems of records; b. dynamically adjusting the queries based on intermediate results to ensure accurate data retrieval.
5. 5 . The system of claim 1 , wherein the AI agent is configured to: a. identify and resolve inconsistencies in the data identities between the multiple disparate systems of record; and b. harmonize the retrieved data using generative AI techniques to ensure consistency and accuracy.
6. 6 . The system of claim 1 , wherein the system does not perform pre-ingestion and curation of data from all systems of records before retrieval.
7. 7 . The system of claim 1 , wherein the AI agent is configured to: a. identify user profile information including security level and operational role; and b. provide only the retrieved data within the security level, formatted to be understood according to the operational role.
8. 8 . The system of claim 1 , wherein one of the AI agents is a monitoring assistant.
9. 9 . The system of claim 1 , wherein one of the AI agents is a communications assistant.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application 63/716,797 filed Nov. 6, 2024. TECHNICAL FIELD The disclosed embodiments relate generally to techniques for accessing data from source systems using artificial intelligence while maintaining security for the data. BACKGROUND Many companies would like to enable their employees to easily access data from source systems within the company using artificial intelligence (AI) such as large language models (LLMs) while ensuring that the employees can only receive answers that they are permitted to see and while preventing the company data from being added to the corpus used for training the LLM. This is complicated due to the great variety in query formatting and data structure of the different systems of record used. There exists a need for this ability to easily and securely search disparate systems of record and receive the requested data that the user is permitted to access. SUMMARY In accordance with some embodiments, a method of securely searching and summarizing results from multiple organizational source systems is disclosed. The method receives input from an operator, then based on the input from the operator, retrieves live data from multiple disparate systems of record using a retrieval-augmented generation (RAG) approach, AI agents, and an AI Assistant Orchestrator. The AI Assistant Orchestrator is aware of all of capabilities of the AI agents and the multiple disparate systems of record including security, data structures, and communication protocols, and the AI Assistant Orchestrator automatically generates requests for the AI agents and queries for the multiple disparate systems of record in their respective correct format and execution order including adaptive calls based on information for preceding request or execution steps. The method also correlates data identities between the multiple disparate systems of record and the AI agents using dynamically generated harmonization steps with generative AI to obtain retrieved data and correlated information; and provides the retrieved data and the correlated information to the operator. In yet another aspect of the present invention, to address the aforementioned problems, some embodiments provide a computer system. The computer system includes one or more processors, memory, and one or more programs. The one or more programs are stored in memory and configured to be executed by the one or more processors. The one or more programs include an operating system and instructions that when executed by the one or more processors cause the computer system to perform any of the methods provided herein. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 illustrates an example system for a digital AI Assistant; and FIG. 2 demonstrates a process flow for natural language and query language processing. Like reference numerals refer to corresponding parts throughout the drawings. DETAILED DESCRIPTION OF EMBODIMENTS Described below are methods, systems, and computer readable storage media that provide a manner of securely searching and summarizing results from organizational source systems, including sending instructions to be executed by the source systems in order to retrieve the requested data. These embodiments are designed to be of particular use for making enterprise engineering information and facility-specific engineering and operational information accessible to end users using a chat feature in a communications platform such as Microsoft Teams. Reference will now be made in detail to various embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure and the embodiments described herein. However, embodiments described herein may be practiced without these specific details. In other instances, well-known methods, procedures, components, and mechanical apparatus have not been described in detail so as not to unnecessarily obscure aspects of the embodiments. The Digital AI Assistant aims to enhance enterprise content search by utilizing natural language processing. This means operators can interact with the system using everyday language rather than complex queries. The Digital AI Assistant can process both structured (organized) and unstructured (free-form) content, offering search results in a range of formats, from raw data to more concise, context-rich summaries tailored to different user personas (roles). This Digital AI Assistant can seamlessly integrate into specific applications, such as Teams, Digital Twins, and the like, offering a user-friendly and intuitive way for enterprise users to directly search various source systems. Any user interface may be used. This integration is sometimes referred to as a “copilot”feature and is a generative AI. The system described herein may be of use for a